Introduction

• Python is a high-level, interpreted programming language
• Easy to learn and understand
• Versatile and widely used in:
  • Web development
  • Data analysis and science
  • Artificial intelligence and machine learning
  • Automation and scripting

Basic Syntax

• Indentation is used to denote block-level structure
• Variables are assigned using the = operator
• Print output using the print() function
• Comments: # for single-line, ''' or """ for multi-line

Data Types

• Integers: int (e.g., 1, 2, 3)
• Floats: float (e.g., 3.14, -0.5)
• Strings: str (e.g., 'hello', "hello")
• Boolean: bool (e.g., True, False)
• List: list (e.g., [1, 2, 3], ['a', 'b', 'c'])
• Dictionary: dict (e.g., {'name': 'John', 'age': 30})

Control Structures

• Conditional statements:
  • if statements: if condition : code
  • elif statements: elif condition : code
  • else statements: else : code
• Loops:
  • for loops: for variable in iterable : code
  • while loops: while condition : code

Functions

• Reusable blocks of code
• Take arguments and return values
• Can be used to:
  • Organize code
  • Reduce repetition
  • Encapsulate complex logic
• Function definition: def function_name (arguments) : code

Modules

• Pre-written code libraries
• Imported using the import statement
• Examples:
  • math: mathematical functions (e.g., sin(), cos())
  • random: random number generation (e.g., randint(), uniform())
  • time: time-related functions (e.g., time(), sleep())

Exception Handling

• Try-except blocks:
  • try block: code that might raise an exception
  • except block: code to handle the exception
• Catching specific exceptions:
  • except ValueError : code
  • except TypeError : code
• Raising exceptions:
  • raise ValueError(message)

Object-Oriented Programming

• Classes:
  • Define custom data types
  • Encapsulate data and behavior
• Objects:
  • Instances of classes
  • Have attributes (data) and methods (behavior)
• Inheritance:
  • Create new classes based on existing ones
  • Inherit attributes and methods

Advanced Topics

• Decorators:
  • Modify function behavior
  • Use @ symbol to apply
• Generators:
  • Special type of iterable
  • Use yield statement to define
• Lambda functions:
  • Small, anonymous functions
  • Use lambda keyword to define

Decorators

def my_decorator(func):
    def wrapper():
        print("Something is happening before the function is called.")
        func()
        print("Something is happening after the function is called.")

    return wrapper


@my_decorator
def say_hello():
    print("Hello!")


say_hello()

Something is happening before the function is called.
Hello!
Something is happening after the function is called.

Generators

def infinite_sequence():
    num = 0
    while True:
        yield num
        num += 1

gen = infinite_sequence()
print(next(gen))

0

print(next(gen))

1

print(next(gen))

2

calcular_area_rectangulo = lambda base, altura: base * altura
print(calcular_area_rectangulo(4, 6))  # Salida: 24

24

Conclusion

• Python is a powerful and versatile language
• Continuously learning and practicing will help you master it
• Explore advanced topics and libraries to become proficient